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cs.LG2026
Why Post-Norm Transformers Collapse: Attention Amplification and Gradient Repair Failure
Xingjian Wang, Qingyu Han, Xiaodong Luo +1
Deep decoder-only Transformers often replace the original Post-Norm architecture with Pre-Norm variants because Post-Norm training is highly sensitive to warmup and learning rate u…
cs.LG2025
QuadEnhancer: Leveraging Quadratic Transformations to Enhance Deep Neural Networks
Qian Chen, Linxin Yang, Akang Wang +2
The combination of linear transformations and non-linear activation functions forms the foundation of most modern deep neural networks, enabling them to approximate highly complex…
cs.LG2025
When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach
Qian Chen, Lei Li, Qian Li +6
A common characteristic in integer linear programs (ILPs) is symmetry, allowing variables to be permuted without altering the underlying problem structure. Recently, GNNs have emer…